热力发电

【目的】新能源大规模并网加剧了电力现货市场的量价波动风险，亟需提升电价预测能力。本文基于山东实际数据，构建LSTM-Transformer混合模型，旨在实现日前电价的精准预测，为市场运营与风险管控提供支撑。【方法】首先，在多维度特征融合过程中，系统分析电价关键影响因素的作用机制，通过皮尔逊相关系数量化其线性关联强度，完善了传统方法因特征单一和依赖经验导致的预测机理不清、精度不足的情况。其次，通过融合长短时记忆（long short-term memory,LSTM）神经网络、自注意力及多层注意力机制优化Transformer模型，构建兼具时序特征提取与全局依赖建模能力的层次化框架，有效增强对电价序列中复杂长短期依赖关系的捕捉效果。此外，采用“时域特征深度挖掘+频域损失约束”策略，通过融合时频域损失提升模型对电价波动与数值的拟合精度。同时，引入相似日模型构建贴合实际的特征样本，进一步提升预测准确性。【结果】通过算例分析，结果表明本文模型即使在影响因素复杂、价格波动剧烈的市场环境中仍能稳定运行，并可显著提升日前电价的预测精度。【结论】所构建的LSTM-Transformer混合模型结合多维度特征融合与时频域损失约束策略，能够有效应对新能源高比例接入带来的量价波动挑战，为电力现货市场的日前电价预测提供了一种高精度、高鲁棒性的解决方案。

[Objective] The large-scale integration of renewable energy into the power grid has significantly exacerbated the risks of volume and price volatility in the electricity spot market, thereby creating an urgent need to improve electricity price forecasting capability. To address this challenge, this study constructs a hybrid LSTMTransformer model based on actual operational data from Shandong Province, China. The primary aim is to achieve accurate day-ahead electricity price forecasting, thereby providing robust support for market operation optimization and risk management and control. [Methods] First, in the process of multi-dimensional feature fusion, the operational mechanisms of key influencing factors on electricity prices are systematically analyzed. The Pearson correlation coefficient is employed to quantify the linear correlation strength between these factors and electricity prices. This approach effectively resolves the problems inherent in traditional methods, such as unclear forecasting mechanisms and insufficient accuracy, which typically arise from relying on single features or empirical judgments. Second, the Transformer model is optimized by integrating LSTM, self-attention, and multi-layer attention mechanisms. This integration establishes a hierarchical framework that combines the strengths of sequential feature extraction and global dependency modeling. As a result, the model effectively enhances the ability to capture the complex long-and short-term dependencies embedded in electricity price sequences. Furthermore, a strategy of “deep mining of time-domain features coupled with frequency-domain loss constraints” is adopted. By fusing timedomain and frequency-domain losses, the model improves its fitting accuracy for both price fluctuations and numerical values. Meanwhile, a similar-day model is introduced to construct feature samples that closely reflect real-world conditions, which further enhances the forecasting accuracy. [Results] Through comprehensive case study analyses, the results demonstrate that the proposed model operates stably even under market conditions characterized by complex influencing factors and severe price fluctuations. The model significantly improves the forecasting accuracy of day-ahead electricity prices compared to conventional approaches. Quantitative evaluations show that the model achieves superior performance in tracking price trends and capturing sudden price spikes, which are common in high-renewable penetration scenarios. [Conclusion] The constructed LSTM-Transformer hybrid model, combined with multi-dimensional feature fusion and the time-frequency domain loss constraint strategy, can effectively address the challenges of volume and price fluctuations brought about by the highproportion integration of renewable energy. It provides a high-precision and high-robustness solution for day-ahead electricity price forecasting in the electricity spot market, thereby contributing to more stable and efficient market operations.